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README.md

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-# network
+#PFENet
+This is the implementation of our paper PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation that has been accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
 
-实验结果
+Get Started
-实验配置
+#Environment
-实验需要的版本例如numpy3.4
+torch==1.4.0 (torch version >= 1.0.1.post2 should be okay to run this repo)
+numpy==1.18.4
+tensorboardX==1.8
+cv2==4.2.0
+Datasets and Data Preparation
+Please download the following datasets:
+
+PASCAL-5i is based on the PASCAL VOC 2012 and SBD where the val images should be excluded from the list of training samples.
+
+COCO 2014.
+
+This code reads data from .txt files where each line contains the paths for image and the correcponding label respectively. Image and label paths are seperated by a space. Example is as follows:
+
+image_path_1 label_path_1
+image_path_2 label_path_2
+image_path_3 label_path_3
+...
+image_path_n label_path_n
+Then update the train/val/test list paths in the config files.
+
+[Update] We have uploaded the lists we use in our paper.
+The train/val lists for COCO contain 82081 and 40137 images respectively. They are the default train/val splits of COCO.
+The train/val lists for PASCAL5i contain 5953 and 1449 images respectively. The train list should be voc_sbd_merge_noduplicate.txt and the val list is the original val list of pascal voc (val.txt).
+To get voc_sbd_merge_noduplicate.txt:
+We first merge the original VOC (voc_original_train.txt) and SBD (sbd_data.txt) training data.
+[Important] sbd_data.txt does not overlap with the PASCALVOC 2012 validation data.
+The merged list (voc_sbd_merge.txt) is then processed by the script (duplicate_removal.py) to remove the duplicate images and labels.
+Run Demo / Test with Pretrained Models
+Please download the pretrained models.
+
+We provide 8 pre-trained models: 4 ResNet-50 based models for PASCAL-5i and 4 VGG-16 based models for COCO.
+
+Update the config file by speficifying the target split and path (weights) for loading the checkpoint.
+
+Execute mkdir initmodel at the root directory.
+
+Download the ImageNet pretrained backbones and put them into the initmodel directory.
+
+Then execute the command:
+
+sh test.sh {*dataset*} {*model_config*}
+
+Example: Test PFENet with ResNet50 on the split 0 of PASCAL-5i:
+
+sh test.sh pascal split0_resnet50
+Train
+Execute this command at the root directory:
+
+sh train.sh {*dataset*} {*model_config*}
+Related Repositories
+This project is built upon a very early version of SemSeg: https://github.com/hszhao/semseg.
+
+Other projects in few-shot segmentation:
+
+OSLSM: https://github.com/lzzcd001/OSLSM
+CANet: https://github.com/icoz69/CaNet
+PANet: https://github.com/kaixin96/PANet
+FSS-1000: https://github.com/HKUSTCV/FSS-1000
+AMP: https://github.com/MSiam/AdaptiveMaskedProxies
+On the Texture Bias for FS Seg: https://github.com/rezazad68/fewshot-segmentation
+SG-One: https://github.com/xiaomengyc/SG-One
+FS Seg Propogation with Guided Networks: https://github.com/shelhamer/revolver
+Many thanks to their greak work!
+
+Citation
+If you find this project useful, please consider citing:
+
+@article{tian2020pfenet,
+  title={Prior Guided Feature Enrichment Network for Few-Shot Segmentation},
+  author={Tian, Zhuotao and Zhao, Hengshuang and Shu, Michelle and Yang, Zhicheng and Li, Ruiyu and Jia, Jiaya},
+  journal={TPAMI},
+  year={2020}
+}